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Accurately detecting sarcasm and context in sentiment analysis is challenging due to the following reasons:

Implicit Meaning: Sarcasm often conveys the opposite of the literal meaning, making it difficult for models that rely on surface-level text analysis to detect true intent. For example, "Oh, great! Another traffic jam!" appears positive but is actually negative.

Lack of Context: Sentiment analysis models typically analyze isolated text without broader conversational or situational context, limiting their ability to understand nuanced expressions.

Cultural and Linguistic Variations: Sarcasm varies across cultures and languages, making it harder for models trained on one dataset to generalize across diverse contexts.

Absence of Vocal and Visual Cues: Text lacks tone, pitch, and facial expressions, which are essential for identifying sarcasm in spoken communication.

Subtle Language Use: The use of irony, ambiguous words, or contradictory statements can confuse models that rely heavily on keywords for sentiment classification.

First thing that comes to my mind is that in sarcasm we often use positive words to express something negative (or vice versa) šŸ§ Which can be hard for a model to detect accurately. This leads me to my second thought: There must be high quality datasets for training

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